Soft and Constrained Hypertree Width

TL;DR

This paper introduces soft hypertree width, a flexible and constraint-aware measure for hypertree decompositions that improves practical query evaluation while maintaining theoretical tractability.

Contribution

It proposes a new framework for computing hypertree decompositions using soft hypertree width, incorporating preferences and constraints for better practical performance.

Findings

Soft hypertree width can be lower than traditional hypertree width.

The framework maintains polynomial-time decision properties.

Preliminary experiments show practical benefits in query evaluation.

Abstract

Hypertree decompositions provide a way to evaluate Conjunctive Queries (CQs) in polynomial time, where the exponent of this polynomial is determined by the width of the decomposition. In theory, the goal of efficient CQ evaluation therefore has to be a minimisation of the width. However, in practical settings, it turns out that there are also other properties of a decomposition that influence the performance of query evaluation. It is therefore of interest to restrict the computation of decompositions by constraints and to guide this computation by preferences. To this end, we propose a novel framework based on candidate tree decompositions, which allows us to introduce soft hypertree width (shw). This width measure is a relaxation of hypertree width (hw); it is never greater than hw and, in some cases, shw may actually be lower than hw. Most importantly, shw preserves the tractability of deciding if a given CQ is below some fixed bound, while offering more algorithmic flexibility. In particular, it provides a natural way to incorporate preferences and constraints into the computation of decompositions. A prototype implementation and preliminary experiments confirm that this novel framework can indeed have a practical impact on query evaluation.